Makoto Yamada
Makoto Yamada
Kyoto University / RIKEN Center for Advanced Intelligence Project (AIP)
Verified email at - Homepage
Cited by
Cited by
Change-point detection in time-series data by relative density-ratio estimation
S Liu, M Yamada, N Collier, M Sugiyama
Neural Networks 43, 72-83, 2013
Intelligent image-activated cell sorting
N Nitta, T Sugimura, A Isozaki, H Mikami, K Hiraki, S Sakuma, T Iino, ...
Cell 175 (1), 266-276. e13, 2018
High-dimensional feature selection by feature-wise kernelized lasso
M Yamada, W Jitkrittum, L Sigal, EP Xing, M Sugiyama
Neural computation 26 (1), 185-207, 2014
Relative density-ratio estimation for robust distribution comparison
M Yamada, T Suzuki, T Kanamori, H Hachiya, M Sugiyama
Neural computation 25 (5), 1324-1370, 2013
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization
G Niu, B Dai, M Yamada, M Sugiyama
Arxiv preprint arXiv:1206.4614, 2012
Beyond ranking: Optimizing whole-page presentation
Y Wang, D Yin, L Jie, P Wang, M Yamada, Y Chang, Q Mei
Proceedings of the Ninth ACM International Conference on Web Search and Data …, 2016
High-throughput imaging flow cytometry by optofluidic time-stretch microscopy
C Lei, H Kobayashi, Y Wu, M Li, A Isozaki, A Yasumoto, H Mikami, T Ito, ...
Nature protocols 13 (7), 1603-1631, 2018
Semi-supervised speaker identification under covariate shift
M Yamada, M Sugiyama, T Matsui
Signal Processing 90 (8), 2353-2361, 2010
Change-point detection with feature selection in high-dimensional time-series data
M Yamada, A Kimura, F Naya, H Sawada
Twenty-Third International Joint Conference on Artificial Intelligence, 2013
Graphlime: Local interpretable model explanations for graph neural networks
Q Huang, M Yamada, Y Tian, D Singh, D Yin, Y Chang
arXiv preprint arXiv:2001.06216, 2020
Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search
M Sugiyama, M Yamada, P Von Buenau, T Suzuki, T Kanamori, ...
Neural Networks 24 (2), 183-198, 2011
No bias left behind: Covariate shift adaptation for discriminative 3d pose estimation
M Yamada, L Sigal, M Raptis
European Conference on Computer Vision, 674-687, 2012
Persistence fisher kernel: A riemannian manifold kernel for persistence diagrams
T Le, M Yamada
arXiv preprint arXiv:1802.03569, 2018
Ultra high-dimensional nonlinear feature selection for big biological data
M Yamada, J Tang, J Lugo-Martinez, E Hodzic, R Shrestha, A Saha, ...
IEEE Transactions on Knowledge and Data Engineering 30 (7), 1352-1365, 2018
Cross-domain object matching with model selection
M Yamada, M Sugiyama
Proceedings of the Fourteenth International Conference on Artificial …, 2011
Clustering-based anomaly detection in multi-view data
A Marcos Alvarez, M Yamada, A Kimura, T Iwata
Proceedings of the 22nd ACM international conference on Information …, 2013
On information-maximization clustering: Tuning parameter selection and analytic solution
M Sugiyama, M Yamada, M Kimura, H Hachiya
ICML, 2011
Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel
YHH Tsai, S Bai, M Yamada, LP Morency, R Salakhutdinov
arXiv preprint arXiv:1908.11775, 2019
A practical guide to intelligent image-activated cell sorting
A Isozaki, H Mikami, K Hiramatsu, S Sakuma, Y Kasai, T Iino, T Yamano, ...
Nature protocols 14 (8), 2370-2415, 2019
Information-maximization clustering based on squared-loss mutual information
M Sugiyama, G Niu, M Yamada, M Kimura, H Hachiya
Neural Computation 26 (1), 84-131, 2014
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